Low Performing Product in Sephora Dataset Product Recommendation Model

Dataset: Sephora Products and Skincare Reviews

Event: Between the Lines: Machine Learning Study Jam – April 4, 2025

Developed by: Gabrielle Ysabel Almirol, Chief Technology Officer, Association of Information Management Benilde

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Project Description

Low Performing Product in Sephora Dataset Product Recommendation Model is a machine learning solution developed for the Between the Lines: Machine Learning Study Jam. The model predicts customer purchasing behavior based on ingredient concentration matching with key chemical ingredients in low-performing sales revenue product P442990 (REN Clean Skincare's Clean Screen Mineral SPF 30 Mattifying Face Sunscreen) and seasonal skincare needs.

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Tech Stack

Category	Tools / Libraries
Programming Language	Python 3.10
ML Framework	CatBoostClassifier
Data Processing	pandas, numpy, Apache Spark
Visualization	matplotlib, seaborn
Modeling	catboost, scikit-learn
Development Tools	Jupyter Notebook, Python, Flask (for API)

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Ingredient Analysis & Benefits

1. Humectants (e.g., Glycerin, Hyaluronic Acid, Panthenol)

• Attract moisture into the skin
• Maintain hydration and reduce flakiness
• Reinforce natural moisture barrier

2. Emollients (e.g., Squalane, Ceramides, Shea Butter)

• Soften and smooth skin texture
• Restore lipid barrier
• Prevent cold-induced irritation

3. Occlusive Agents (e.g., Dimethicone, Petrolatum, Beeswax)

• Form a protective seal to lock in moisture
• Shield skin from wind and low humidity
• Extend humectant/emollient effects

4. Antioxidants (e.g., Vitamin E, Green Tea Extract, Niacinamide)

• Reduce oxidative stress and inflammation
• Support skin repair
• Boost moisturizing effectiveness

5. UV & Pollution Protectants (e.g., Zinc Oxide, Titanium Dioxide, Licorice Root Extract)

• Guard against UV even in winter
• Minimize irritation and pigmentation
• Aid in seasonal skin recovery

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Model-Based Ingredient Optimization

Utilizing CatBoostClassifier, the model predicts customer preference likelihood using ingredient concentrations in P442990 and similar products during cold months (Jan–Apr).

Label	Definition
1	Higher-than-median concentration match (> 0.075)
0	Lower-than-median concentration match (≤ 0.075)

Classification Report

Category	Precision	Recall	F1-Score	Support
0 (Low Match)	1.00	1.00	1.00	61,020
1 (High Match)	1.00	1.00	1.00	135,444

• Correlation (ingredient concentration vs cold-weather preference): 0.2826

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Key Takeaways

• Cold-weather buyers prefer higher concentrations of humectants and occlusives
• Ingredient optimization according to seasonal skincare needs increases product relevance and sales potential
• High accuracy and recall affirm the model's effectiveness in aligning product formulation with consumer behavior

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Roles & Responsibilities

• Spearheaded a predictive recommendation model using CatBoostClassifier to classify customers based on key ingredient concentration matching by identifying low-performing sales revenue product P442990 and other products during cold months (January–April).

  • Target labels: 1 (above median concentration match of 0.075) and 0 (below).

  • Achieved:

    • 99.93% Accuracy
    • 99.95% F1 Score
    • 99.89% Precision
    • 100% Recall
    • Trained on over 8k products and 1 million reviews.

• Refined feature engineering integrating:

  • Seasonal skincare trends
  • Customer purchase history
  • Chemical ingredient composition Optimized product formulation strategies and uncovered a moderate correlation (0.2826) between ingredient concentration and cold-weather skincare preferences (2019–2023).

• Designed visual analytics tools to examine:

  • Ingredient effectiveness
  • Customer segmentation
  • Demand fluctuations Provided actionable insights for targeted marketing and product positioning in winter skincare campaigns.

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🎥 Resources

• 🔗 Between the Lines Study Jam Recording
• 🔗 Between the Lines Model
• 🔗 Between the Lines AI/ML API